Регламент проверки контрагента в компании
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Federal state institution Gufsin LIU-10 of the Russian Federation on of the Novosibirsk region
OGRN: 1025400517284
TIN: 5401125990
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Activities for management and exploitation of prisons, corrective colonies and other places of imprisonment, also for rendering rehabilitation help to former prisoners
Monitoring
Notes
Reports
Contact information
15,1
mln ₽, 2004
Revenue
+₽15mln
Chief of institution
Vishtalyuk Igor Nikolaevich
78
Reliability Score
Chief of institution
Vishtalyuk Igor Nikolaevich
31
years
Established
December 17, 1993
village Razdolnoye
15,1
mln ₽, 2004
Revenue
+₽15mln
645
Connections
+1
last week
Employees
No data
Stats Over Time
| Participation | Total number of cases | For the last year |
|---|---|---|
|
✖
Plaintiff
|
19 cases (▒▒▒▒▒ ₽) | 0 cases (▒▒▒▒▒ ₽) |
|
✖
Defendant
|
32 cases (▒▒▒▒▒ ₽) | 6 cases (▒▒▒▒▒ ₽) |
|
✖
Third party
|
No cases as third party | |
|
✖
Other party
|
4 cases (▒▒▒▒▒ ₽) | 0 cases (▒▒▒▒▒ ₽) |
Positive outcome — ▒▒%
▒▒▒▒▒₽
- 9 cases (▒▒▒▒▒ ₽) won
- 2 cases (▒▒▒▒▒ ₽) partially won
- 0 cases (▒▒▒▒▒ ₽) not lost
Negative outcome — ▒▒%
▒▒▒▒▒₽
- 12 cases (▒▒▒▒▒ ₽) lost
- 1 case (▒▒▒▒▒ ₽) partially lost
- 0 cases (▒▒▒▒▒ ₽) not won
Type of dispute
%
Administrative
%
Civil
%
Bankruptcy-related
%
Other
Current cases
As defendant (2)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
Completed cases
As plaintiff (19)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
As defendant (30)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
As other party (4)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
Data sources
Information about company arbitration cases is collected from public sources.
Accurate binding to a company can be created only if the information source contains its TIN and OGRN values. If identification details are not specified, the binding is created by using linguistic algorithms. Although these algorithms are being constantly improved, some information may be absent.
If you have noticed an error or lack of information, please let us know about it. Let's improve Seldon.Basis together!
Data sources
Information about company arbitration cases is collected from public sources.
Accurate binding to a company can be created only if the information source contains its TIN and OGRN values. If identification details are not specified, the binding is created by using linguistic algorithms. Although these algorithms are being constantly improved, some information may be absent.
If you have noticed an error or lack of information, please let us know about it. Let's improve Seldon.Basis together!
